An enterprise system is a large-scale system of integrated applications that helps organization to manage business functions, and automate many back office functions. The enterprise system integrates all facets of an operation, including products planning, development, manufacturing, sales and marketing, and thus acts as a backbone for the enterprise. Unplanned downtimes of the enterprise system due to unforeseen failures in hardware or software are extremely costly to the organization. The source of unplanned downtime can be in any of layers that make up the complete software and hardware environment, and it is hard to trace the source due to large size of the enterprise system and large scale of usage of the enterprise system. For the same reason, it is very hard to build physics based dynamical system models that can analyze enterprise system performance.
Massive or large data sets are generated in today's information-centric world by ubiquitous communication, imaging systems, mobile devices, surveillance cameras and drones, medical and e-commerce platforms, social networking sites. These large data sets need to be processed appropriately to provide timely insights, improved decision quality, risk mitigation of unplanned events, and appropriate planning of enterprise operations. These large data sets are processed by large size enterprise systems. An average dialog response time (referred to as ‘response time’ of the enterprise system hereafter) is an important indicator of a health of the enterprise system, and is affected by many factors associated with an operating system, or databases or application servers. Predicting well in advance, a potential failure of the large enterprise system is extremely important, so that timely interventions can be actuated to prevent performance degradation. Generally the system response time is used as a measure for system performance, and the factors affecting the system response time are identified and concurrently measured.
There are some solutions provided in the art to predict performance of the enterprise system, however these solutions are more theoretical in nature and lack in practical use case. Further, the conventional prediction of enterprise system performance is not considering complexity of the data and hence not useful for real-time decision making. Hence, existing solutions including sophisticated techniques, cannot be directly adapted to predict enterprise system response time accurately. In view of complexity of the large enterprise systems, only periodic and concurrent measurements of the response time (output) of the enterprise system, and associated input factors can be carried out. The advanced prediction of the response time can be used to design appropriate predictive maintenance schedules of the enterprise system to take preventive actions against enterprise system outages. The conventional techniques fail to predict the response time in advance accurately.